• Corpus ID: 238260052

Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective

@inproceedings{Wang2021VolatilityPC,
  title={Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective},
  author={Weichen Wang and Ran An and Ziwei Zhu},
  year={2021}
}
Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data… 

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SHOWING 1-10 OF 25 REFERENCES
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